Abstract. A modified form of tracer–tracer correlations of N2O and O3 has been
used as a tool for the evaluation of atmospheric photochemical models.
Applying this method, monthly averages of N2O and O3 are derived for
both hemispheres by partitioning the data into altitude (or potential
temperature) bins and then averaging over a fixed interval of N2O. In a
previous study, the method has been successfully applied to the evaluation of
two chemical transport models (CTMs) and one chemistry–climate model (CCM)
using a 1 yr climatology derived from the Odin Sub-Millimetre Radiometer
(Odin/SMR). However, the applicability of a 1 yr climatology of monthly
averages of N2O and O3 has been questioned due to the inability of some
CCMs to simulate a specific year for the evaluation of CCMs. In this study,
satellite measurements from Odin/SMR, the Aura Microwave Limb Sounder
(Aura/MLS), the Michelson Interferometer for Passive Atmospheric Sounding on
ENVISAT (ENVISAT/MIPAS), and the Cryogenic Infrared Spectrometers and
Telescopes for the Atmosphere (CRISTA-1 and CRISTA-2) as well as model
simulations from the Whole Atmosphere Community Climate Model (WACCM)
are considered. By using seven to eight years of satellite measurements
derived between 2003 and 2010 from Odin/SMR, Aura/MLS, ENVISAT/MIPAS and six
years of model simulations from WACCM, the interannual variability of lower
stratospheric monthly averages of N2O and O3 is assessed. It is shown
that the interannual variability of the monthly averages of N2O and O3
is low, and thus can be easily distinguished from model deficiencies.
Furthermore, it is investigated why large differences are found between Odin/SMR
observations and model simulations from the Karlsruhe Simulation Model of the
Middle Atmosphere (KASIMA) and the atmospheric general circulation model
ECHAM5/Messy1 for the Northern and Southern Hemisphere tropics
(0° to 30° N and 0° to −30° S,
respectively). The differences between model simulations and observations are
most likely caused by an underestimation of the quasi-biennial oscillation
and tropical upwelling by the models as well as due to biases and/or
instrument noise from the satellite instruments. A realistic consideration of
the QBO in the model reduces the differences between model simulation and
observations significantly. Finally, an intercomparison between Odin/SMR,
Aura/MLS, ENVISAT/MIPAS and WACCM was performed. The comparison shows that
these data sets are generally in good agreement, although some known biases of
the data sets are clearly visible in the monthly averages. Nevertheless, the
differences caused by the uncertainties of the satellite data sets are
sufficiently small and can be clearly distinguished from model deficiencies.
Thus, the method applied in this study is not only a valuable tool for model
evaluation, but also for satellite data intercomparisons.